Post-traumatic stress disorder (PTSD) is a debilitating condition resulting from trauma exposure that involves persistent trauma-related thoughts and nightmares, avoidance of trauma-related stimuli, distorted beliefs about oneself or others, distorted cognitions about the cause of the trauma, sleep disturbance, and irritable behavior or angry outbursts.
Mental healthcare providers need assessment methods that reliably and accurately determine PTSD diagnostic status, especially among veterans and military personnel who are seeking treatment for the disorder in record numbers. Currently, standardized diagnostic interviews, such as the Structured Clinical Interview for the DSM-IV (SCID) and the Clinician-Administered PTSD Scale (CAPS), are the gold standard for determining PTSD diagnostic status. However, in order to best differentiate PTSD from similar psychiatric disorders and to address potential response bias, multimethod assessment procedures that incorporate standardized diagnostic interviews, self-reports, psychophysiological assessment, and other methods are recommended.
Advances in psychophysiological signal processing and machine learning techniques, together with more affordable sensors (e.g., electroencephalography (EEG)), show improved screening performance relative to structured interviews. In particular, a growing body of work proposes machine learning approaches for assessing psychological health disorders such as PTSD, depression, and mild Traumatic Brain Injury (mTBI), using modalities including heart rate, heart rate with galvanic skin response (GSR), EEG, electrocardiography (EKG), voice quality, speech, speech in dialog systems, and modality combinations.
Psychophysiological responses to structured protocols carry relevant information for psychological health disorder classification, and the combination of psychophysiological and audio-visual features can improve disorder detection rates relative to using audio-visual features alone. The impact of this result is diminished in real world applications because psychophysiological sensors (i.e., EEG, GSR, and EKG) generally are more resource intensive to apply and more aversive to patients than non-intrusive sensors (i.e., audio and video recording), thus decreasing engagement rates.